Visual odometry refers to the estimation of the path of a camera from solely from video images taken by the camera. The term “visual odometry” was created by Nister due to its similarity to wheel odometry. Wheel odometry estimates the distance traveled by a vehicle based on rotations of the vehicle's wheels. Visual odometry estimates motion, not only the distance traveled, but also the path or trajectory (X, Y, Z) coordinates and camera orientation at each point), traveled by a camera based on analysis of images captured by a camera in successive video frames. Such a path or trajectory can be used to re-trace the path of the camera or the object to which the camera is attached. Applications of video odometry include robotics, location services, turn-by-turn navigation, and augmented reality. For example, if GPS communications are not available, visual odometry can provide a trajectory to be followed if it is desirable to retrace a path.
Existing visual odometry algorithms rely on a triangulation step in order to reconstruct tracked features. The reconstructed features are then tracked between video sequences in order to maintain a uniform scale of camera trajectory. Reconstructing tracked features using triangulation for each frame is computationally intensive. Accordingly, there exists a need for improved methods for visual odometry that avoids or reduces the need for triangulation for each frame and is less computationally intensive than existing visual odometry methods.